Object-Oriented ML Model Management via Segmented Class Libraries
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Solution Overview
Problem
Current machine learning model management systems are ill-suited for the diversity of contemporary models, leading to inefficiencies in development, maintenance, and adaptation, as they often rely on manual approaches that fail to facilitate modularity and reusability across different use cases.
Innovation Solution
The implementation of object-oriented modeling techniques, including the use of modeling-object classes and quality-management classes, allows for the creation of modular, reusable machine learning models through object manipulation functions, decoupling development from dataset characteristics and labeling techniques, and employing distributed computing frameworks like Apache Spark.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual approaches are used for managing machine learning models, then flexibility in handling diverse models is maintained, but development time and maintenance complexity increase significantly
Solution Approach 1:
The patent segments machine learning model management into modular components including modeling-object classes for model creation and quality-management classes for validation. This segmentation allows independent development and maintenance of different model types while providing standardized interfaces, thereby reducing development time without sacrificing adaptability to diverse model requirements.
Solution Approach 2:
The patent creates universal class libraries that can manage multiple types of machine learning models through common interfaces and structures. The modeling-object classes and quality-management classes are designed to be model-agnostic, enabling the same framework to handle diverse models (e.g., classification, regression, clustering) without requiring manual customization for each model type.
2Adaptability or versatility
If manual approaches are used for managing machine learning models, then customization for specific use cases is possible, but reusability and modularity are reduced
Solution Approach 1:
The patent divides the model management system into distinct, reusable classes: modeling-object classes for model instantiation and quality-management classes for validation. Each class is self-contained and can be independently modified or replaced, enabling customization for specific use cases while maintaining overall system modularity and reusability across different projects.
Solution Approach 2:
The patent introduces quality-management classes as intermediary components between model creation and deployment. These classes provide standardized validation interfaces that work with various model types, allowing customization at the model level while maintaining consistent quality checks through the intermediary layer, thus preserving both adaptability and modularity.
3Device complexity
If monolithic modeling approaches are used, then simplicity in implementation is maintained, but scalability and integration complexity increase
Solution Approach 1:
The patent segments the previously monolithic modeling approach into separate modeling-object classes and quality-management classes. This segmentation maintains implementation simplicity through clear class definitions while enabling scalability by allowing independent addition and configuration of model types and validation rules without restructuring the entire system.
Solution Approach 2:
The patent transitions from a single-dimensional monolithic model to a multi-dimensional class-based architecture. By organizing models and quality checks into separate classes with standardized interfaces, the system gains an additional dimension of organization that simplifies implementation while enabling scalable integration across multiple models and use cases.
4Productivity
If numerous predictive models are deployed to meet diverse objectives, then analytical capability is improved, but management complexity and technology debt increase
Solution Approach 1:
The patent creates universal class libraries that can manage numerous predictive models through common interfaces. The modeling-object classes provide standardized model creation capabilities, while quality-management classes offer unified validation mechanisms. This universality allows deployment of many diverse models to meet different analytical objectives while reducing management complexity through consistent handling procedures.
Solution Approach 2:
The patent segments model management into independent, reusable classes that can be applied across multiple models. This segmentation reduces technology debt by eliminating redundant code for each model and provides a systematic approach to managing numerous predictive models, thereby improving analytical capability without proportionally increasing management complexity.
Data Source
AI summary
Provided is a process including: writing modelling-object classes using object-oriented modelling of the modelling methods, the modelling-object classes being members of a set of class libraries; writing quality-management classes using object-oriented modelling of quality management, the quality-management classes being members of the set of class libraries; scanning modelling-object classes in the set of class libraries to determine modelling-object class definition information; scanning quality-management classes in the set of class libraries to determine quality-management class definition information; using the modelling-object class definition information and the quality-management class definition information to produce object manipulation functions that allow a quality management system to access methods and attributes of modelling-object classes to manipulate objects of the modelling-object classes; and using the modelling-object class definition information and the quality-management class definition information to produce access to the object manipulation functions.